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    Öğe
    Application of regression kriging and machine learning methods to estimate soil moisture constants in a semi-arid terrestrial area
    (Elsevier Sci Ltd, 2023) Tuncay, Tulay; Alaboz, Pelin; Dengiz, Orhan; Baskan, Oguz
    In the current study, the use of regression-kriging (RK), artificial neural networks (ANN), support vector machines (SVM), and random forest (RF) methods from machine learning algorithms, were used to estimate field capacity (FC), permanent wilting point (PWP), available water content (AWC) and their performance was compared. A data set obtained from 354 surface soil samples taken randomly, mostly from agricultural areas is used. The soil data set includes pH, EC, calcium carbonate equivalent (CaCO3 equivalent), particle size distribution, and bulk density (BD) values. The results showed that while FC showed a negative strong correlation (p < 0.001) with sand (r:-0.69), BD (r:-0.85), and silt (r:-0.47), it showed a positive strong correlation (p < 0.001) with C (r: 0.90). Similarly, PWP showed a negative strong correlation with (p < 0.001) sand (r:-0.73), BD (r:0.88), and silt (r:-0.42) but a positive strong correlation (p < 0.001) with C (r: 0.90). While AWC showed a negative strong correlation (p < 0.001) with sand (r:-0.61), BD (r:-0.76), it found a positive strong correlation (p < 0.001) with FC (r: 0.97), clay (r: 0.83), and PWP (r: 0.74). In the stepwise regression results showed that particle size were prominent as the most important factor in the regression equation created for FC, PWP and AWC. Moreover, FC is the most important factor to predict AWC. For the soil FC, ANN was best with excellent accuracy (RPD = 2.71), followed by SVM (2.42), RF (2.21) while RK was poor accuracy (1.10 and 1.04). Similarly, among the machine learning algorithms (RF and SVM), ANN obtained superiority by producing lower RRMSE (7.84%), RMSE (2.83%), MAE (2.37%), MAPE (7.45%), with the largest Lin's concordance correlation coefficient (LCCC) (0.961) compared to other methods. For PWP and AWC, ANN was the best algorithm with excellent and good accuracy RPD 3.17 and 1.95 respecively. In addition, other machine learning algorithms have been the same value range in terms of LCCC. Therefore, we recommend the ANN machine-learning algorithm is more favorable to predict FC, PWP and AWC than both RK and other machine learning methods.
  • [ X ]
    Öğe
    Assessing soil fertility index based on remote sensing and gis techniques with field validation in a semiarid agricultural ecosystem
    (Academic Press Ltd- Elsevier Science Ltd, 2021) Tuncay, Tulay; Kilic, Seref; Dedeoglu, Mert; Dengiz, Orhan; Baskan, Oguz; Bayramin, Ilhami
    Among the greatest challenges of the arid and semiarid regions is the need for more crop production to meet the increasing demand of the growing population. This study aimed to compare SFI classes with both yield values and vegetation index values derived from satellite images. A total of 281 soil samples were taken at a 1-km resolution in order to quantify the spatial dynamics of soil physical, chemical and fertility indicators. Of the study area, 40.0% had very high fertile and high fertile soils, while 26.7% of the area had moderately fertile soils. Only about one-third of the total area had low and very low fertility. These results were validated using a 3-year yield values belong to parcels, and vegetation index derived from Sentinel 2A images. A strong relationship of SFI with yield (r2 = 0.88) and RE-OSAVI (r2 = 0.83) was found. Therefore, we suggested that SFI can be used to determine the sufficiency potential of soils for plant growing and management according to sustainable principles in similar ecologies provided that similar sample size should used.